Abstract

This paper studies a stochastic differential game of transboundary pollution abatement between two kinds of ecological compensation and the abatement policy, in which the learning by doing is taken into account. Emission and pollution abatement between upstream and downstream region in the same basin is a Stackelberg game, and the downstream regions provide economic compensation for pollution abatement in the upstream region. We discuss the feedback Nash equilibrium strategies of proportional compensation and investment compensation, and it is found that an appropriate ecological compensation ratio can improve the investment level of pollution abatement in the two regions by accumulating experience in the process of learning by doing. In the long term, the investment compensation mechanism is an effective transboundary pollution abatement measure that can continuously reduce the water pollution stock in the upstream and downstream.

Highlights

  • Environmental degradation and its relationship with economic activities have been a subject of intense debate in the academic community and among policy makers

  • Due to the long-term effect of investment compensation mechanism, with the increase of investment compensation level in the downstream region, the quality of water environment will be improved in a certain time

  • By solving dynamic equations and numerical simulation, we find that all the results show that the two regions will promote the abatement levels and reduce the pollution stocks when the learning by doing is considered

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Summary

Introduction

Environmental degradation and its relationship with economic activities have been a subject of intense debate in the academic community and among policy makers. Under the model of proportional compensation mechanism, the problem is a Stackelberg differential game that determines the amount of pollution emission and the compensation ratio from the downstream of the basin and determines the amount of pollution emission and abatement in the upstream region.

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